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--- |
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license: cc-by-nc-4.0 |
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base_model: Helsinki-NLP/opus-mt-tc-big-en-ar |
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metrics: |
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- bleu |
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model-index: |
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- name: Terjman-Large-v2 |
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results: [] |
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datasets: |
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- atlasia/darija_english |
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language: |
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- ar |
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- en |
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--- |
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# Terjman-Large-v2 (240M params) |
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Our model is built upon the powerful Transformer architecture, leveraging state-of-the-art natural language processing techniques. |
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It is a fine-tuned version of [Helsinki-NLP/opus-mt-tc-big-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-ar) on a the [darija_english](atlasia/darija_english) dataset enhanced with curated corpora ensuring high-quality and accurate translations. |
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This model is an impovement of the previous version [Terjman-Large](atlasia/Terjman-Large). |
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The finetuning was conducted using a **A100-40GB** and took **17 hours**. |
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## Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-04 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 30 |
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## Usage |
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Using our model for translation is simple and straightforward. |
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You can integrate it into your projects or workflows via the Hugging Face Transformers library. |
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Here's a basic example of how to use the model in Python: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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# Load the tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("atlasia/Terjman-Large-v2") |
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model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Terjman-Large-v2") |
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# Define your Moroccan Darija Arabizi text |
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input_text = "Your english text goes here." |
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# Tokenize the input text |
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input_tokens = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True) |
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# Perform translation |
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output_tokens = model.generate(**input_tokens) |
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# Decode the output tokens |
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output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True) |
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print("Translation:", output_text) |
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``` |
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## Example |
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Let's see an example of transliterating Moroccan Darija Arabizi to Arabic: |
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**Input**: "Hello my friend, how's life in Morocco" |
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**Output**: "سالام صاحبي كيف الأحوال فالمغرب" |
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## Limiations |
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This version has some limitations mainly due to the Tokenizer. |
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We're currently collecting more data with the aim of continous improvements. |
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## Feedback |
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We're continuously striving to improve our model's performance and usability and we will be improving it incrementaly. |
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If you have any feedback, suggestions, or encounter any issues, please don't hesitate to reach out to us. |
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## Framework versions |
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- Transformers 4.39.2 |
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- Pytorch 2.2.2+cpu |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
